Societal Implicit Memory and his Speed on Tracking Extrema over Dynamic Environments using Self-Regulatory Swarms Export

@article{citeulike:407741, abstract = {In order to overcome difficult dynamic optimization and environment extrema tracking problems, we propose a Self-Regulated Swarm (SRS) algorithm which hybridizes the advantageous characteristics of Swarm Intelligence as the emergence of a societal environmental memory or cognitive map via collective pheromone laying in the landscape (properly balancing the exploration/exploitation nature of the search strategy), with a simple Evolutionary mechanism that trough a direct reproduction procedure linked to local environmental features is able to self-regulate the above exploratory swarm population, speeding it up globally. In order to test his adaptive response and robustness, we have recurred to different dynamic multimodal complex functions as well as to Dynamic Optimization Control (DOC) problems. Measures were made for different dynamic settings and parameters such as, environmental upgrade frequencies, landscape changing speed severity, type of dynamic (linear or circular), and to dramatic changes on the algorithmic search purpose over each test environment (e.g. shifting the extrema). Finally, comparisons were made with traditional Genetic Algorithms (GA), Bacterial Foraging Optimization Algorithms (BFOA), as well as with more recently proposed Co-Evolutionary approaches. SRS, were able to demonstrate quick adaptive responses, while outperforming the results obtained by the other approaches. Additionally, some successful behaviors were found: SRS was able not only to achieve quick adaptive responses, as to maintaining a number of different solutions, while adapting to new unforeseen extrema; the possibility to spontaneously create and maintain different subpopulations on different peaks, emerging different exploratory corridors with intelligent path planning capabilities; the ability to request for new agents over dramatic changing periods, and economizing those foraging resources over periods of stabilization. Finally, results prove that the present SRS collective swarm of bio-inspired agents is able to track about 65\% of moving peaks traveling up to ten times faster than the velocity of a single individual composing that precise swarm tracking system. This emerged behavior is probably one of the most interesting ones achieved by the present work.}, author = {Ramos, Vitorino and Fernandes, Carlos and Rosa, Agostinho C.}, citeulike-article-id = {407741}, citeulike-linkout-0 = {http://alfa.ist.utl.pt/~cvrm/staff/vramos/ref_66.html}, keywords = {adaptive\_computation, ant\_colony\_systems, artificial\_intelligence, artificial\_life, collective\_computing, collective\_intelligence, collective\_systems, complex\_systems, distributed\_computing, dynamic\_environments, implicit\_memory, memory, optimization, reaction\_speed, self-organization, stigmergy, swarm\_intelligence}, posted-at = {2005-11-24 18:59:33}, priority = {0}, title = {Societal Implicit Memory and his Speed on Tracking Extrema over Dynamic Environments using Self-Regulatory Swarms}, url = {http://alfa.ist.utl.pt/~cvrm/staff/vramos/ref_66.html} }

TY - JOUR ID - citeulike:407741 L3 - citeulike-article-id:407741 N2 - In order to overcome difficult dynamic optimization and environment extrema tracking problems, we propose a Self-Regulated Swarm (SRS) algorithm which hybridizes the advantageous characteristics of Swarm Intelligence as the emergence of a societal environmental memory or cognitive map via collective pheromone laying in the landscape (properly balancing the exploration/exploitation nature of the search strategy), with a simple Evolutionary mechanism that trough a direct reproduction procedure linked to local environmental features is able to self-regulate the above exploratory swarm population, speeding it up globally. In order to test his adaptive response and robustness, we have recurred to different dynamic multimodal complex functions as well as to Dynamic Optimization Control (DOC) problems. Measures were made for different dynamic settings and parameters such as, environmental upgrade frequencies, landscape changing speed severity, type of dynamic (linear or circular), and to dramatic changes on the algorithmic search purpose over each test environment (e.g. shifting the extrema). Finally, comparisons were made with traditional Genetic Algorithms (GA), Bacterial Foraging Optimization Algorithms (BFOA), as well as with more recently proposed Co-Evolutionary approaches. SRS, were able to demonstrate quick adaptive responses, while outperforming the results obtained by the other approaches. Additionally, some successful behaviors were found: SRS was able not only to achieve quick adaptive responses, as to maintaining a number of different solutions, while adapting to new unforeseen extrema; the possibility to spontaneously create and maintain different subpopulations on different peaks, emerging different exploratory corridors with intelligent path planning capabilities; the ability to request for new agents over dramatic changing periods, and economizing those foraging resources over periods of stabilization. Finally, results prove that the present SRS collective swarm of bio-inspired agents is able to track about 65% of moving peaks traveling up to ten times faster than the velocity of a single individual composing that precise swarm tracking system. This emerged behavior is probably one of the most interesting ones achieved by the present work. TI - Societal Implicit Memory and his Speed on Tracking Extrema over Dynamic Environments using Self-Regulatory Swarms KW - adaptive_computation KW - ant_colony_systems KW - artificial_intelligence KW - artificial_life KW - collective_computing KW - collective_intelligence KW - collective_systems KW - complex_systems KW - distributed_computing KW - dynamic_environments KW - implicit_memory KW - memory KW - optimization KW - reaction_speed KW - self-organization KW - stigmergy KW - swarm_intelligence AU - Ramos, Vitorino AU - Fernandes, Carlos AU - Rosa, Agostinho UR - http://alfa.ist.utl.pt/~cvrm/staff/vramos/ref_66.html ER -